Mining explicit rules for software process evaluation

ISCAS OpenIR

	Mining explicit rules for software process evaluation
	Sun, Chengnian (1); Du, Jing (2); Chen, Ning (1); Khoo, Siau-Cheng (1); Yang, Ye (2)
	2013
会议名称	2013 International Conference on Software and Systems Process, ICSSP 2013
页码	118-125
会议日期	May 18, 2013 - May 19, 2013
会议地点	San Francisco, CA, United states
收录类别	EI
出版地	Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
ISBN	9781450320627
部门归属	(1) School of Computing, National University of Singapore, Singapore; (2) Lab for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, China
摘要	We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM.; We present an approach to automatically discovering explicit rules for software process evaluation from evaluation histories. Each rule is a conjunction of a subset of attributes in a process execution, characterizing why the execution is normal or anomalous. The discovered rules can be used for stakeholder as expertise to avoid mistakes in the future, thus improving software process quality; it can also be used to compose a classifier to automatically evaluate future process execution. We formulate this problem as a contrasting itemset mining task, and employ the branch-and-bound technique to speed up mining by pruning search space. We have applied the proposed approach to four real industrial projects in a commercial bank. Our empirical studies show that the discovered rules can precisely pinpoint the cause of all anomalous executions, and the classifier built on the rules is able to accurately classify unknown process executions into the normal or anomalous class. Copyright 2013 ACM.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16645
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Sun, Chengnian ,Du, Jing ,Chen, Ning ,et al. Mining explicit rules for software process evaluation[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013:118-125.